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Sub-terahertz (THz) wireless communication links require low-SWaP (size, weight, and power) software defined radio (SDR) modems to achieve efficient and reliable data transmission. This research presents the design, development, and experimentation of an SDR system operating in the 135-150 GHz frequency range, utilizing simple I/Q modulation techniques such as differential binary phase shift keying (DBPSK). The system integrates advanced components, including Virginia Diodes (VDI) 110-170 GHz compact upconverter (CCU) and compact downconverter (CCD), high-gain lens horn antennas from Anteral (40 dBi), and the Xilinx RF-SoC ZCU-111 for real time DSP. A 500 MHz IF is implemented on RF-SoC with baseband bandwidth 64 MHz and data rate 64 Mbps via DBPSK modulation. For 20 dBm transmit power at 147 GHz, the nearfield SNR was measured to be 55 dB at 1m lens-to-lens separation for a baseband of 64 MHz. Simulation models of propagation predict 64 Mbps is possibly viable at up to 2 km in a point-to-point connection for a BER of < 10−3. The SDR was realized on the Xilinx PYNQ platform, offering a user-friendly interface while being adaptable to high data rate applications. This digital design is particularly suited for deployment in scenarios such as vehicle-to-vehicle communication, backhaul networks, and data center level interconnects. The research explored challenges related to synchronization, signal integrity, and environmental sensitivity, which are critical for maintaining reliable communication in a 147 GHz channel. A real-time text messaging application demonstrated correct operation of the PYNQ modem. 

The radio spectrum is a scarce and extremely valuable resource that demands careful real-time monitoring and dynamic resource allocation. Dynamic spectrum access (DSA) is a new paradigm for managing the radio spectrum, which requires AI/ML-driven algorithms for optimum performance under rapidly changing channel conditions and possible cyber-attacks in the electromagnetic domain. Fast sensing across multiple directions using array processors, with subsequent AI/ML-based algorithms for the sensing and perception of waveforms that are measured from the environment is critical for providing decision support in DSA. As part of directional and wideband spectrum perception, the ability to finely channelize wideband inputs using efficient Fourier analysis is much needed. However, a fine-grain fast Fourier transform (FFT) across a large number of directions is computationally intensive and leads to a high chip area and power consumption. We address this issue by exploiting the recently proposed approximate discrete Fourier transform (ADFT), which has its own sparse factorization for real-time implementation at a low complexity and power consumption. The ADFT is used to create a wideband multibeam RF digital beamformer and temporal spectrum-based attention unit that monitors 32 discrete directions across 32 sub-bands in real-time using a multiplierless algorithm with low computational complexity. The output of this spectral attention unit is applied as a decision variable to an intelligent receiver that adapts its center frequency and frequency resolution via FFT channelizers that are custom-built for real-time monitoring at high resolution. This two-step process allows the fine-gain FFT to be applied only to directions and bands of interest as determined by the ADFT-based low-complexity 2D spacetime attention unit. The fine-grain FFT provides a spectral signature that can find future use cases in neural network engines for achieving modulation recognition, IoT device identification, and RFI identification. Beamforming and spectral channelization algorithms, a digital computer architecture, and early prototypes using a 32-element fully digital multichannel receiver and field programmable gate array (FPGA)-based high-speed software-defined radio (SDR) are presented. 

The ability to sense propagating electromagnetic plane waves based on their directions of arrival (DOAs) is fundamental to a range of radio frequency (RF) sensing, communications, and imaging applications. This paper introduces an algorithm for the wideband true time delay digital delay Vandermonde matrix (DVM), utilizing Thiran fractional delays that are useful for realizing RF sensors having multiple look DOA support. The digital DVM algorithm leverages sparse matrix factorization to yield multiple simultaneous RF beams for low-complexity sensing applications. Consequently, the proposed algorithm offers a reduction in circuit complexity for multi-beam digital wideband beamforming systems employing Thiran fractional delays. Unlike finite impulse response filter-based approaches which are wideband but of a high filter order, the Thiran filters trade usable bandwidth in favor of low-complexity circuits. The phase and group delay responses of Thiran filters with delays of a fractional sampling period will be demonstrated. Thiran filters show favorable results for sample delay blocks with a temporal oversampling factor of three. Thiran fractional delays of orders three and four are mapped to Xilinx FPGA RF-SoC technologies for evaluation. The preliminary results of the APF-based Thiran fractional delays on FPGA can potentially be used to realize DVM factorizations using application-specific integrated circuit (ASIC) technology.